CK was supported by Alfred Deakin Postdoctoral Research Fellowship and Deakin University CRGS ( ) under Grant 0000031139. įunding: The study was supported by the Natural Science Foundation of Shandong Province ( ) under Grant ZR2015FQ016 (PL), the National Natural Science Foundation of China ( ) under Grant 61601263 (PL), the International Postdoctoral Exchange Fellowship from the China Postdoctoral Council ( ) under Grant 20150042 (PL). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data used in this study are third party data and are publicly available from the Bonn database at the following link. Received: ApAccepted: FebruPublished: March 15, 2018Ĭopyright: © 2018 Li et al. PLoS ONE 13(3):Įditor: Maxim Bazhenov, University of California San Diego, UNITED STATES Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.Ĭitation: Li P, Karmakar C, Yearwood J, Venkatesh S, Palaniswami M, Liu C (2018) Detection of epileptic seizure based on entropy analysis of short-term EEG. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96) and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). We performed feature selection and trained classifiers based on a cross-validation process. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)–were used that have valid outputs for any given data lengths. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs and 2) classifying ictal from interictal EEGs. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Entropy measures that assess signals’ complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful.

Epilepsy eeg vs normal eeg